import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sns.set(style="whitegrid")
import matplotlib.pyplot as plt
from collections import Counter
%matplotlib inline
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# Any results you write to the current directory are saved as output.
# ignore warnings
import warnings
warnings.filterwarnings('ignore')
fifa19 = pd.read_csv(r'C:\Users\Jithender\Music\11th_resume project\Seaborn\FIFA.csv', index_col=0)
fifa19.head()
| ID | Name | Age | Photo | Nationality | Flag | Overall | Potential | Club | Club Logo | ... | Composure | Marking | StandingTackle | SlidingTackle | GKDiving | GKHandling | GKKicking | GKPositioning | GKReflexes | Release Clause | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 158023 | L. Messi | 31 | https://cdn.sofifa.org/players/4/19/158023.png | Argentina | https://cdn.sofifa.org/flags/52.png | 94 | 94 | FC Barcelona | https://cdn.sofifa.org/teams/2/light/241.png | ... | 96.0 | 33.0 | 28.0 | 26.0 | 6.0 | 11.0 | 15.0 | 14.0 | 8.0 | €226.5M |
| 1 | 20801 | Cristiano Ronaldo | 33 | https://cdn.sofifa.org/players/4/19/20801.png | Portugal | https://cdn.sofifa.org/flags/38.png | 94 | 94 | Juventus | https://cdn.sofifa.org/teams/2/light/45.png | ... | 95.0 | 28.0 | 31.0 | 23.0 | 7.0 | 11.0 | 15.0 | 14.0 | 11.0 | €127.1M |
| 2 | 190871 | Neymar Jr | 26 | https://cdn.sofifa.org/players/4/19/190871.png | Brazil | https://cdn.sofifa.org/flags/54.png | 92 | 93 | Paris Saint-Germain | https://cdn.sofifa.org/teams/2/light/73.png | ... | 94.0 | 27.0 | 24.0 | 33.0 | 9.0 | 9.0 | 15.0 | 15.0 | 11.0 | €228.1M |
| 3 | 193080 | De Gea | 27 | https://cdn.sofifa.org/players/4/19/193080.png | Spain | https://cdn.sofifa.org/flags/45.png | 91 | 93 | Manchester United | https://cdn.sofifa.org/teams/2/light/11.png | ... | 68.0 | 15.0 | 21.0 | 13.0 | 90.0 | 85.0 | 87.0 | 88.0 | 94.0 | €138.6M |
| 4 | 192985 | K. De Bruyne | 27 | https://cdn.sofifa.org/players/4/19/192985.png | Belgium | https://cdn.sofifa.org/flags/7.png | 91 | 92 | Manchester City | https://cdn.sofifa.org/teams/2/light/10.png | ... | 88.0 | 68.0 | 58.0 | 51.0 | 15.0 | 13.0 | 5.0 | 10.0 | 13.0 | €196.4M |
5 rows × 88 columns
fifa19.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 18207 entries, 0 to 18206 Data columns (total 88 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 18207 non-null int64 1 Name 18207 non-null object 2 Age 18207 non-null int64 3 Photo 18207 non-null object 4 Nationality 18207 non-null object 5 Flag 18207 non-null object 6 Overall 18207 non-null int64 7 Potential 18207 non-null int64 8 Club 17966 non-null object 9 Club Logo 18207 non-null object 10 Value 18207 non-null object 11 Wage 18207 non-null object 12 Special 18207 non-null int64 13 Preferred Foot 18159 non-null object 14 International Reputation 18159 non-null float64 15 Weak Foot 18159 non-null float64 16 Skill Moves 18159 non-null float64 17 Work Rate 18159 non-null object 18 Body Type 18159 non-null object 19 Real Face 18159 non-null object 20 Position 18147 non-null object 21 Jersey Number 18147 non-null float64 22 Joined 16654 non-null object 23 Loaned From 1264 non-null object 24 Contract Valid Until 17918 non-null object 25 Height 18159 non-null object 26 Weight 18159 non-null object 27 LS 16122 non-null object 28 ST 16122 non-null object 29 RS 16122 non-null object 30 LW 16122 non-null object 31 LF 16122 non-null object 32 CF 16122 non-null object 33 RF 16122 non-null object 34 RW 16122 non-null object 35 LAM 16122 non-null object 36 CAM 16122 non-null object 37 RAM 16122 non-null object 38 LM 16122 non-null object 39 LCM 16122 non-null object 40 CM 16122 non-null object 41 RCM 16122 non-null object 42 RM 16122 non-null object 43 LWB 16122 non-null object 44 LDM 16122 non-null object 45 CDM 16122 non-null object 46 RDM 16122 non-null object 47 RWB 16122 non-null object 48 LB 16122 non-null object 49 LCB 16122 non-null object 50 CB 16122 non-null object 51 RCB 16122 non-null object 52 RB 16122 non-null object 53 Crossing 18159 non-null float64 54 Finishing 18159 non-null float64 55 HeadingAccuracy 18159 non-null float64 56 ShortPassing 18159 non-null float64 57 Volleys 18159 non-null float64 58 Dribbling 18159 non-null float64 59 Curve 18159 non-null float64 60 FKAccuracy 18159 non-null float64 61 LongPassing 18159 non-null float64 62 BallControl 18159 non-null float64 63 Acceleration 18159 non-null float64 64 SprintSpeed 18159 non-null float64 65 Agility 18159 non-null float64 66 Reactions 18159 non-null float64 67 Balance 18159 non-null float64 68 ShotPower 18159 non-null float64 69 Jumping 18159 non-null float64 70 Stamina 18159 non-null float64 71 Strength 18159 non-null float64 72 LongShots 18159 non-null float64 73 Aggression 18159 non-null float64 74 Interceptions 18159 non-null float64 75 Positioning 18159 non-null float64 76 Vision 18159 non-null float64 77 Penalties 18159 non-null float64 78 Composure 18159 non-null float64 79 Marking 18159 non-null float64 80 StandingTackle 18159 non-null float64 81 SlidingTackle 18159 non-null float64 82 GKDiving 18159 non-null float64 83 GKHandling 18159 non-null float64 84 GKKicking 18159 non-null float64 85 GKPositioning 18159 non-null float64 86 GKReflexes 18159 non-null float64 87 Release Clause 16643 non-null object dtypes: float64(38), int64(5), object(45) memory usage: 12.4+ MB
fifa19['Body Type'].value_counts()
Normal 10595 Lean 6417 Stocky 1140 Messi 1 C. Ronaldo 1 Neymar 1 Courtois 1 PLAYER_BODY_TYPE_25 1 Shaqiri 1 Akinfenwa 1 Name: Body Type, dtype: int64
f, ax = plt.subplots(figsize=(8,6))
x = fifa19['Age']
ax = sns.distplot(x, bins=10)
plt.show()
f, ax = plt.subplots(figsize=(8,6))
x = fifa19['Age']
x = pd.Series(x, name="Age variable")
ax = sns.distplot(x, bins=10)
plt.show()
f, ax = plt.subplots(figsize=(8,6))
x = fifa19['Age']
ax = sns.distplot(x, bins=10, vertical = True)
plt.show()
f, ax = plt.subplots(figsize=(8,6))
x = fifa19['Age']
x = pd.Series(x, name="Age variable")
ax = sns.kdeplot(x)
plt.show()
f, ax = plt.subplots(figsize=(8,6))
x = fifa19['Age']
x = pd.Series(x, name="Age variable")
ax = sns.kdeplot(x, shade=True, color='r')
plt.show()
f, ax = plt.subplots(figsize=(8,6))
x = fifa19['Age']
ax = sns.distplot(x, kde=False, rug=True, bins=10)
plt.show()
f, ax = plt.subplots(figsize=(8,6))
x = fifa19['Age']
ax = sns.distplot(x, hist=False, rug=True, bins=10)
plt.show()
fifa19['Preferred Foot'].nunique()
2
fifa19['Preferred Foot'].value_counts()
Right 13948 Left 4211 Name: Preferred Foot, dtype: int64
f, ax = plt.subplots(figsize=(8, 6))
sns.countplot(x="Preferred Foot", data=fifa19, color="c")
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.countplot(x="Preferred Foot", hue="Real Face", data=fifa19)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.countplot(y="Preferred Foot", data=fifa19, color="c")
plt.show()
g = sns.catplot(x="Preferred Foot", kind="count", palette="ch:.25", data=fifa19)
fifa19['International Reputation'].nunique()
5
fifa19['International Reputation'].value_counts()
1.0 16532 2.0 1261 3.0 309 4.0 51 5.0 6 Name: International Reputation, dtype: int64
f, ax = plt.subplots(figsize=(8, 6))
sns.stripplot(x="International Reputation", y="Potential", data=fifa19)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.stripplot(x="International Reputation", y="Potential", data=fifa19, jitter=0.01)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.stripplot(x="International Reputation", y="Potential", hue="Preferred Foot",
data=fifa19, jitter=0.2, palette="Set2", dodge=True)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.stripplot(x="International Reputation", y="Potential", hue="Preferred Foot",
data=fifa19, palette="Set2", size=20, marker="D",
edgecolor="gray", alpha=.25)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.boxplot(x=fifa19["Potential"])
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.boxplot(x="International Reputation", y="Potential", data=fifa19)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.boxplot(x="International Reputation", y="Potential", hue="Preferred Foot", data=fifa19, palette="Set3")
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.violinplot(x=fifa19["Potential"])
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.violinplot(x="International Reputation", y="Potential", data=fifa19)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.violinplot(x="International Reputation", y="Potential", hue="Preferred Foot", data=fifa19, palette="muted")
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.violinplot(x="International Reputation", y="Potential", hue="Preferred Foot",
data=fifa19, palette="muted", split=True)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.pointplot(x="International Reputation", y="Potential", data=fifa19)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.pointplot(x="International Reputation", y="Potential", hue="Preferred Foot", data=fifa19)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.pointplot(x="International Reputation", y="Potential", hue="Preferred Foot", data=fifa19, dodge=True)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.pointplot(x="International Reputation", y="Potential", hue="Preferred Foot",
data=fifa19, markers=["o", "x"], linestyles=["-", "--"])
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.barplot(x="International Reputation", y="Potential", data=fifa19)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.barplot(x="International Reputation", y="Potential", hue="Preferred Foot", data=fifa19)
plt.show()
from numpy import median
f, ax = plt.subplots(figsize=(8, 6))
sns.barplot(x="International Reputation", y="Potential", data=fifa19, estimator=median)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.barplot(x="International Reputation", y="Potential", data=fifa19, ci=68)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.barplot(x="International Reputation", y="Potential", data=fifa19, ci="sd")
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.barplot(x="International Reputation", y="Potential", data=fifa19, capsize=0.2)
plt.show()
g = sns.relplot(x="Overall", y="Potential", data=fifa19)
f, ax = plt.subplots(figsize=(8, 6))
sns.scatterplot(x="Height", y="Weight", data=fifa19)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
ax = sns.lineplot(x="Stamina", y="Strength", data=fifa19)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
ax = sns.regplot(x="Overall", y="Potential", data=fifa19)
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
ax = sns.regplot(x="Overall", y="Potential", data=fifa19, color= "g", marker="+")
plt.show()
f, ax = plt.subplots(figsize=(8, 6))
sns.regplot(x="International Reputation", y="Potential", data=fifa19, x_jitter=.01)
plt.show()
g= sns.lmplot(x="Overall", y="Potential", data=fifa19)
g= sns.lmplot(x="Overall", y="Potential", hue="Preferred Foot", data=fifa19)
g= sns.lmplot(x="Overall", y="Potential", hue="Preferred Foot", data=fifa19, palette="Set1")
g= sns.lmplot(x="Overall", y="Potential", col="Preferred Foot", data=fifa19)
g = sns.FacetGrid(fifa19, col="Preferred Foot")
g = sns.FacetGrid(fifa19, col="Preferred Foot")
g = g.map(plt.hist, "Potential")
g = sns.FacetGrid(fifa19, col="Preferred Foot")
g = g.map(plt.hist, "Potential", bins=10, color="r")
g = sns.FacetGrid(fifa19, col="Preferred Foot")
g = (g.map(plt.scatter, "Height", "Weight", edgecolor="w").add_legend())
g = sns.FacetGrid(fifa19, col="Preferred Foot", height=5, aspect=1)
g = g.map(plt.hist, "Potential")
fifa19_new = fifa19[['Age', 'Potential', 'Strength', 'Stamina', 'Preferred Foot']]
g = sns.PairGrid(fifa19_new)
g = g.map(plt.scatter)
g = sns.PairGrid(fifa19_new)
g = g.map_diag(plt.hist)
g = g.map_offdiag(plt.scatter)
g = sns.PairGrid(fifa19_new, hue="Preferred Foot")
g = g.map_diag(plt.hist)
g = g.map_offdiag(plt.scatter)
g = g.add_legend()
g = sns.PairGrid(fifa19_new, hue="Preferred Foot")
g = g.map_diag(plt.hist, histtype="step", linewidth=3)
g = g.map_offdiag(plt.scatter)
g = g.add_legend()
g = sns.PairGrid(fifa19_new, vars=['Age', 'Stamina'])
g = g.map(plt.scatter)
g = sns.PairGrid(fifa19_new)
g = g.map_upper(plt.scatter)
g = g.map_lower(sns.kdeplot, cmap="Blues_d")
g = g.map_diag(sns.kdeplot, lw=3, legend=False)
g = sns.JointGrid(x="Overall", y="Potential", data=fifa19)
g = g.plot(sns.regplot, sns.distplot)
import matplotlib.pyplot as plt
g = sns.JointGrid(x="Overall", y="Potential", data=fifa19)
g = g.plot_joint(plt.scatter, color=".5", edgecolor="white")
g = g.plot_marginals(sns.distplot, kde=False, color=".5")
g = sns.JointGrid(x="Overall", y="Potential", data=fifa19, space=0)
g = g.plot_joint(sns.kdeplot, cmap="Blues_d")
g = g.plot_marginals(sns.kdeplot, shade=True)
g = sns.JointGrid(x="Overall", y="Potential", data=fifa19, height=5, ratio=2)
g = g.plot_joint(sns.kdeplot, cmap="Reds_d")
g = g.plot_marginals(sns.kdeplot, color="r", shade=True)
f, ax = plt.subplots(figsize=(8, 6))
ax = sns.regplot(x="Overall", y="Potential", data=fifa19);
sns.lmplot(x="Overall", y="Potential", col="Preferred Foot", data=fifa19, col_wrap=2, height=5, aspect=1)
<seaborn.axisgrid.FacetGrid at 0x161dfa7dd50>
def sinplot(flip=1):
x = np.linspace(0, 14, 100)
for i in range(1, 7):
plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)
sinplot()
sns.set()
sinplot()
sns.set_style("whitegrid")
sinplot()
sns.set_style("dark")
sinplot()
sns.set_style("white")
sinplot()
sns.set_style("ticks")
sinplot()